Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery

• Published in: Advances in experimental medicine and biology Vol. 1462; p. 307
• Main Authors: Lee, Cheng-Chia, Yang, Huai-Che, Wu, Hsiu-Mei, Lin, Yen-Yu, Lu, Chia-Feng, Peng, Syu-Jyun, Wu, Yu-Te, Sheehan, Jason P, Guo, Wan-Yuo
• Format: Journal Article
• Language: English
• Published: United States 2024
• Subjects: Algorithms; Artificial Intelligence; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - radiotherapy; Brain Neoplasms - surgery; Computer Simulation; Deep Learning; Humans; Machine Learning; Radiation Oncology - methods; Radiosurgery - methods; Deep learning; Segmentation; Machine learning; Prediction; Radiosurgery; Detection; Gamma Knife; Artificial intelligence; Stereotactic; Radiomics
• ISSN: 0065-2598
• DOI: 10.1007/978-3-031-64892-2_18

The chapter explores the extensive integration of artificial intelligence (AI) in healthcare systems, with a specific focus on its application in stereotactic radiosurgery. The rapid evolution of AI technology has led to promising developments in this field, particularly through the utilization of machine learning and deep learning models. The diverse implementation of AI algorithms was developed from various aspects of radiosurgery, including the successful detection of spontaneous tumors and the automated delineation or segmentation of lesions. These applications show potential for extension to longitudinal treatment follow-up. Additionally, the chapter highlights the established use of machine learning algorithms, particularly those incorporating radiomic-based analysis, in predicting treatment outcomes. The discussion encompasses current achievements, existing limitations, and the need for further investigation in the dynamic intersection of AI and radiosurgery.